Tomographic imaging systems such as diagnostic CT scanners, tomosynthesis systems, and cone-beam CT (CBCT) systems require a known system geometry in order to perform accurate image reconstruction. Because image quality can be sensitive to sub-mm errors in geometry and system geometry can change over time, most CT/CBCT systems employ some form of routine (e.g., daily) geometric calibration to provide up-to-date system geometry for each patient acquisition. However, errors in geometric calibration persist, resulting in blurry images, ring artifacts, geometric distortion, and other artifacts. Reasons for such errors include overly simplistic models of system geometry, change introduced to the system (whether intentional or unintentional), scan-to-scan variability such as system jitter, and non-reproducible orbits such as custom-designed trajectories. Additionally, (rigid) patient motion can be corrected by updating the system geometry on a per-projection basis to effectively move the system with respect to the virtually fixated patient. Therefore, a method for providing an accurate scan-specific geometric description of the acquired projections would be highly desired.
System geometry for each projection in a CT/CBCT acquisition can be divided into two sets of parameters—intrinsic and extrinsic. Intrinsic parameters define the x-ray source position relative to the x-ray detector and can be parameterized by 3 degrees of freedom (DOF). Most CT/CBCT systems have fixed intrinsic geometry, including diagnostic CT scanners, dedicated dental, extremity, and head CBCT scanners, and some mobile C-arms, as illustrated in FIG. 1. FIG. 1 also illustrates a robotic C-arm with adjustable geometry and a variable orbit. Extrinsic parameters relate the imaging system pose to the patient reference frame and are represented by a 6-DOF translation and rotation (or equivalently, 6-DOF patient pose relative to the detector coordinate frame). Calibrating the projection geometry for fixed-geometry systems is inherently a 6-DOF problem (per projection) given that the intrinsic parameters are constrained and can be well-calibrated; however, the intrinsics are known to vary from scan to scan for reasons including gravity-induced flex and system jitter. A full 9-DOF calibration that jointly estimates the extrinsic and intrinsic parameters (per projection) would address the issue of unknown intrinsics, including for adjustable-geometry systems. Once the 9 DOFs (per projection) are known, the system geometry can then be represented by a set of projection matrices (PMs)—one PM per projection.
A number of “conventional” geometric calibration methods exist for tomographic systems that estimate the geometry of each projection using a prior calibration scan and then used for image reconstruction. For example, in CBCT, methods include the use of a specially designed calibration phantom, such as a spiral BB phantom, a phantom with two rings of BBs, or other patterns of markers. However, there may be a change in geometry or other inconsistencies between the previously acquired geometric calibration scan and the current patient scan, or the geometry/trajectory may be irreproducible altogether (e.g., custom-designed trajectories on the Zeego). Scan-specific (“on-line”) geometric calibration methods have been proposed that simultaneously acquire fiducial markers with the patient scan; however, this requires placing markers on or next to the patient and within the imaging field of view, which is a cumbersome (and sometimes impossible) process that superimposes markers onto the projections and is detrimental to image reconstruction, or are limited to niche applications where small high-contrast objects are already within the patient (e.g., brachytherapy). Other scan-specific methods propose the use of additional sensors to capture the source and detector positions, but introduce additional hardware and workflow complexity. Image-based “auto-focus” methods require time-consuming reconstructions in each function evaluation and/or optimize image quality metrics over a set of few simple parameters that describe the overall geometry of the system (e.g., assuming a circular orbit), rather than the full geometry of each projection.
In many applications of CT/CBCT systems that require geometric calibration (e.g., intraoperative CBCT), a 3D volumetric representation of the patient (e.g., preoperative CT) is often available. In this case, the patient himself can be used as a fiducial, which obviates the need for external fiducials or trackers that are often inaccurate and a burden to workflow. In this concept of “patient as the fiducial,” the feature-rich anatomy of a patient can be used by a 3D-2D registration process to provide “self-calibrated” geometry, given that some of the anatomy is known ahead of time (e.g., preoperative CT). Despite the desire for shared anatomical structures between the current scan and the prior scan, the current scan is still needed for providing up-to-date visualization in applications including targeting soft-tissue structures (e.g., tumors), monitoring anatomical change (e.g., bleeds, tumor resection), and/or verifying interventional device placement (e.g., stents, coils, pedicle screws), and the self-calibration should be robust against any such changes. Previously proposed 3D-2D registration methods required several projections to perform a 3D-3D registration between the preoperative CT and reconstruction from sparse projections and required a known geometric calibration. Registration of a prior 3D volume to individual 2D projections (e.g., radiographs) has also been previously proposed, but required prior calibration, approximate geometric knowledge, or imaging the patient with an additional calibration fiducial of known shape.
Existing geometric calibration for tomographic systems, includes calibration performed “off-line” with dedicated phantoms (generally containing small, high-contrast markers such as BBs in known locations), calibration performed “on-line” with a known fiducial marker placed into the imaging field of view and acquired simultaneously with the patient, and “auto-focus” methods that adjust geometric parameters to improve image quality. However, none can provide a complete, scan-specific description of the system geometry without introducing additional workflow complexities that can be cumbersome (and sometimes impossible) to use. For example, the “off-line” calibration methods cannot be used for non-reproducible trajectories, nor can they account for scan-to-scan variability such as system jitter. The “on-line” calibration methods require use of additional markers or hardware that interferes with workflow and often with image reconstruction. Lastly, the “auto-focus” methods do not provide a complete geometric description and instead usually only tune a few parameters due to the computational challenges associated with tuning all available parameters.
Accordingly, there is a need in the art for a method of self-calibrating projection geometry for volumetric image reconstruction.